> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# PDF Knowledge Base

> Learn how to use local PDF files in your knowledge base.

The **PDFKnowledgeBase** reads **local PDF** files, converts them into vector embeddings and loads them to a vector database.

## Usage

<Note>
  We are using a local PgVector database for this example. [Make sure it's running](https://docs-v1.agno.com/vectordb/pgvector)
</Note>

```shell theme={null}
pip install pypdf
```

```python knowledge_base.py theme={null}
from agno.knowledge.pdf import PDFKnowledgeBase, PDFReader
from agno.vectordb.pgvector import PgVector

pdf_knowledge_base = PDFKnowledgeBase(
    path="data/pdfs",
    # Table name: ai.pdf_documents
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
    reader=PDFReader(chunk=True),
)
```

Then use the `pdf_knowledge_base` with an Agent:

```python agent.py theme={null}
from agno.agent import Agent

agent = Agent(
    knowledge=pdf_knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")
```

#### PDFKnowledgeBase also supports async loading.

```shell theme={null}
pip install qdrant-client
```

We are using a local Qdrant database for this example. [Make sure it's running](https://docs-v1.agno.com/vectordb/qdrant)

```python async_knowledge_base.py theme={null}
import asyncio

from agno.agent import Agent
from agno.knowledge.pdf import PDFKnowledgeBase, PDFReader
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "pdf-reader"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")

# Create a knowledge base with the PDFs from the data/pdfs directory
knowledge_base = PDFKnowledgeBase(
    path="data/pdf",
    vector_db=vector_db,
    reader=PDFReader(chunk=True),
)

# Create an agent with the knowledge base
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)

if __name__ == "__main__":
    # Comment out after first run
    asyncio.run(knowledge_base.aload(recreate=False))

    # Create and use the agent
    asyncio.run(agent.aprint_response("How to make Thai curry?", markdown=True))
```

## Params

| Parameter | Type                               | Default       | Description                                                                                          |
| --------- | ---------------------------------- | ------------- | ---------------------------------------------------------------------------------------------------- |
| `path`    | `Union[str, Path]`                 | -             | Path to `PDF` files. Can point to a single PDF file or a directory of PDF files.                     |
| `reader`  | `Union[PDFReader, PDFImageReader]` | `PDFReader()` | A `PDFReader` or `PDFImageReader` that converts the `PDFs` into `Documents` for the vector database. |

`PDFKnowledgeBase` is a subclass of the [AgentKnowledge](/reference/knowledge/base) class and has access to the same params.

## Developer Resources

* View [Sync loading Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/pdf_kb.py)
* View [Async loading Cookbook](https://github.com/agno-agi/agno/blob/main/cookbook/agent_concepts/knowledge/pdf_kb_async.py)